Majority of cancer research today takes place in biology and medicine. Artificial intelligence plays a minor supporting role in this progress if at all. In this talk, I will focus on machine learning models that are already making difference in clinical practice. Examples of these methods include automatic reading of imaging data, large-scale analytics over patient records, and improved models of disease progression. I will show tools that are already implemented in clinic and are used to inform patient care. This part of the talk draws on active collaboration with physicians at MGH Cancer Center.

At the second part of the talk, I will push beyond standard tools, introducing new functionalities and avoiding annotation-hungry training paradigms ill-suited for clinical practice. Specifically, I will focus on interpretable neural models that provide rationales underlying their predictions,Â andÂ transfer models that robustly handle data in different domains.

Bio:

Regina Barzilay is a Delta Electronics professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Her research interests are in natural language processing, applications of deep learning to chemistry and oncology. She is a recipient of various awards including the NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards at NAACL and ACL. In 2017, she received a MacArthur fellowship, an ACL fellowship and an AAAI fellowship. She received her Ph.D. in Computer Science from Columbia University, and spent a year as a postdoc at Cornell University.